--- license: apache-2.0 base_model: sentence-transformers/all-MiniLM-L6-v2 pipeline_tag: feature-extraction library_name: onnx tags: - dakera - ai-agent-memory - agent-memory - long-term-memory - rag - self-hosted - mcp - onnx - mirror ---

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all-MiniLM-L6-v2

Lightweight embedding model for Dakera ยท a verified mirror by Dakera, the self-hosted memory engine for AI agents

Dakera: dakera.ai Docs: dakera.ai/docs GitHub: Dakera-AI License: Apache-2.0 Original: sentence-transformers/all-MiniLM-L6-v2

> An unmodified, checksum-verified copy of the files [Dakera](https://dakera.ai) uses from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). The model is by **sentence-transformers**; all credit goes to its authors. ## In Dakera The lightest embedding model Dakera supports, for small hosts: `DAKERA_MODEL=minilm`. Dakera fetches this embedding model from this repository first and from the original repositories if it is unreachable, so installs never depend on a single source. Both are checked against the same SHA-256, and the [Dakera Docker image](https://github.com/Dakera-AI/dakera-deploy) ships the default models ready to run, with no download at startup. ## Files Copied byte for byte from: | File | Source | Size | |---|---|---| | `onnx/model.onnx` | [Xenova/all-MiniLM-L6-v2](https://huggingface.co/Xenova/all-MiniLM-L6-v2) | 90.4 MB | | `onnx/model_quantized.onnx` | [Xenova/all-MiniLM-L6-v2](https://huggingface.co/Xenova/all-MiniLM-L6-v2) | 23.0 MB | | `config.json` | [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 612 B | | `model.safetensors` | [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 90.9 MB | | `tokenizer.json` | [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 466.2 kB |
SHA-256 checksums ```text 759c3cd2b7fe7e93933ad23c4c9181b7396442a2ed746ec7c1d46192c469c46e onnx/model.onnx afdb6f1a0e45b715d0bb9b11772f032c399babd23bfc31fed1c170afc848bdb1 onnx/model_quantized.onnx 953f9c0d463486b10a6871cc2fd59f223b2c70184f49815e7efbcab5d8908b41 config.json 53aa51172d142c89d9012cce15ae4d6cc0ca6895895114379cacb4fab128d9db model.safetensors be50c3628f2bf5bb5e3a7f17b1f74611b2561a3a27eeab05e5aa30f411572037 tokenizer.json ```
## Licence and credit Apache-2.0, the same as the original model (see [`LICENSE`](https://huggingface.co/DakeraAI/all-MiniLM-L6-v2/blob/main/LICENSE)). For what the model does, how it was trained and how to cite it, see the [original model card](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). ## About Dakera **[Dakera](https://dakera.ai) is a self-hosted memory engine for AI agents.** It gives agents long-term memory that survives sessions: hybrid vector and full-text retrieval, reranking, entity extraction and importance decay, in a single Rust binary that runs on your own servers, with an MCP server for Claude, Cursor and other agents. ```bash docker run -p 3000:3000 -e DAKERA_ROOT_API_KEY=dk-mykey ghcr.io/dakera-ai/dakera:latest ``` | | | |---|---| | ๐ŸŒ Website | [dakera.ai](https://dakera.ai) | | ๐Ÿ“š Documentation | [dakera.ai/docs](https://dakera.ai/docs) | | ๐Ÿ’ป GitHub | [github.com/Dakera-AI](https://github.com/Dakera-AI) | | ๐Ÿš€ Self-hosting (Docker, Kubernetes, Helm) | [dakera-deploy](https://github.com/Dakera-AI/dakera-deploy) | | ๐Ÿ”Œ MCP server | [dakera-mcp](https://github.com/Dakera-AI/dakera-mcp) | | ๐Ÿงฐ SDKs | [Python](https://github.com/Dakera-AI/dakera-py) ยท [TypeScript](https://github.com/Dakera-AI/dakera-js) ยท [Go](https://github.com/Dakera-AI/dakera-go) ยท [Rust](https://github.com/Dakera-AI/dakera-rs) | | ๐Ÿ’ผ LinkedIn | [Dakera AI](https://www.linkedin.com/company/dakera-ai) | Questions or feedback: [amine@dakera.ai](mailto:amine@dakera.ai).